Efficient Virtual Machine Resource Management for Media Cloud Computing

( Mohammad Mehedi Hassan ),( Biao Song ),( Ahmad Almogren ),( M. Shamim Hossain ),( Atif Alamri ),( Mohammed Alnuem ),( Muhammad Mostafa Monowar ),( M. Anwar Hossain ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.5

Virtual Machine (VM) resource management is crucial to satisfy the Quality of Service (QoS) demands of various multimedia services in a media cloud platform. To this end, this paper presents a VM resource allocation model that dynamically and optimally utilizes VM resources to satisfy QoS requirements of media-rich cloud services or applications. It additionally maintains high system utilization by avoiding the over-provisioning of VM resources to services or applications. The objective is to 1) minimize the number of physical machines for cost reduction and energy saving; 2) control the processing delay of media services to improve response time; and 3) achieve load balancing or overall utilization of physical resources. The proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed a Mixed Integer Linear Programming (MILP) model, as well as heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme outperforms the existing VM allocation schemes in a media cloud environment, in terms of cost reduction, response time reduction and QoS guarantee.

A Novel Framework for Resource Orchestration in OpenStack Cloud Platform

( Afaq Muhammad ),( Wang-cheol Song ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.11

This work is mainly focused on two major topics in cloud platforms by using OpenStack as a case study: management and provisioning of resources to meet the requirements of a service demanded by remote end-user and relocation of virtual machines (VMs) requests to offload the encumbered compute nodes. The general framework architecture contains two subsystems: 1) An orchestrator that allows to systematize provisioning and resource management in OpenStack, and 2) A resource utilization based subsystem for vibrant VM relocation in OpenStack. The suggested orchestrator provisions and manages resources by: 1) manipulating application program interfaces (APIs) delivered by the cloud supplier in order to allocate/control/manage storage and compute resources; 2) interrelating with software-defined networking (SDN) controller to acquire the details of the accessible resources, and training the variations/rules to manage the network based on the requirements of cloud service. For resource provisioning, an algorithm is suggested, which provisions resources on the basis of unused resources in a pool of VMs. A sub-system is suggested for VM relocation in a cloud computing platform. The framework decides the proposed overload recognition, VM allocation algorithms for VM relocation in clouds and VM selection.

Network-aware Virtual Machines Allocation Technique for High Performance Cloud

Jung-Lok Yu,Chan-Ho Choi,Du-Seok Jin,Jongsuk Ruth Lee,Hee-Jung Byun 한국정보통신학회 2014 2016 INTERNATIONAL CONFERENCE Vol.6 No.1

Virtualized computing cloud has been considered as a well-known resource provisioning and computing environment due to its advantages like maximized resource utilization, isolated performance, and customizable runtimes, etc. Specifically, however, efficient resource (i.e., virtual machine; VM) allocation techniques are strongly required to deliver higher quality of Internet and/or computing services for a broad spectrum of domains, such as science, education, and business areas, etc. In this paper, we propose a novel dynamic, self-adaptive VM allocation technique considering network resource contention on Xen virtualization environment. Using generic virtual cluster and job management framework, we also practically analyze the impact of various system parameters and job characteristics on the performance of our technique. The results show that our approach outperforms others, reducing average job response (by up to 37%) and execution (by up to 22.3%) times.

Energy-aware Multi-dimensional Resource Allocation Algorithm in Cloud Data Center

( Jiawei Nie ),( Juan Luo ),( Luxiu Yin ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.9

Energy-efficient virtual resource allocation algorithm has become a hot research topic in cloud computing. However, most of the existing allocation schemes cannot ensure each type of resource be fully utilized. To solve the problem, this paper proposes a virtual machine (VM) allocation algorithm on the basis of multi-dimensional resource, considering the diversity of user`s requests. First, we analyze the usage of each dimension resource of physical machines (PMs) and build a D-dimensional resource state model. Second, we introduce an energy-resource state metric (PAR) and then propose an energy-aware multi-dimensional resource allocation algorithm called MRBEA to allocate resources according to the resource state and energy consumption of PMs. Third, we validate the effectiveness of the proposed algorithm by real-world datasets. Experimental results show that MRBEA has a better performance in terms of energy consumption, SLA violations and the number of VM migrations.

VCPU-PCPU Mapping with Various Selection Permission in Cloud Computing

Ziyu Fang,Cheulwoo Ro 한국정보통신학회 2018 2016 INTERNATIONAL CONFERENCE Vol.10 No.1

The effective allocation of resources is important in the virtual system. This paper mainly describes the different processing power, how to carry out the effective allocation of resources and performance analysis in models with selectable permissions and fully free random allocation of resources. The PCPUs have different processing speed, and the VCPUs have the different number of cores in order to simulate the real environment. Compared with VCPU-PCPU mapping with full selection permission, VCPU-PCPU mapping with various selection permission is more efficient and stable in most cases.

클라우드 자원 브로커에서 확장성 있는 가상 머신 할당 기법을 이용한 비용 적응형 작업 스케쥴링 알고리즘

( Ye Ren ),김성환 ( Seong Hwan Kim ),강동기 ( Dong Ki Kang ),김병상 ( Byung Sang Kim ),윤찬현 ( Chan Hyun Youn ) 한국정보처리학회 2012 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.1 No.3

사용자들은 자신의 작업을 처리하기 위해 자신에게만 한정된 가상 컴퓨팅 자원을 클라우드 서비스 제공자로부터 할당 받아 타 사용자로부터 독립된 환경에서 작업을 처리하게 된다. 이를 자동화된 방법으로 최적화를 대신 수행해주기 위한 모델로 브로커 미들웨어가 제시되었고 마감시간을 만족하는 이내에서 자원 이용률을 높이는 접근법으로 필요 가상 머신의 숫자를 줄여 비용을 절약한다. 이를 다루는 많은 논문들에서 작업 스케줄링은 기존 사용자들간의 독립을 보장하여 하나의 가상 머신이 하나의 작업에 한정된 가상 머신에서 처리하는 방식으로 다루어지고 있다. 하지만 기존의 SRSV 방식에서는 높은 정도의 다중 프로그래밍 작업이 아닐 경우 시스템을 효율적으로 사용하지 못한다. 이에 본 논문에서는 해당 자원을 마감시간과 스래싱(thrashing), 문맥 전환(context switching)에 따른 성능 저하를 고려한 상태에서 다중 프로그래밍 정도를 높여 낭비되는 자원을 최소화하여 비용을 절약하려고 한다. 실험 결과를 통해 제안하는 방법이 제약조건 이내에서 기존의 방식에 비해 좀 더좋은 가격 대비 성능을 가지는 것을 보인다. Cloud service users request dedicated virtual computing resource from the cloud service provider to process jobs in independent environment from other users. To optimize this process with automated method, in this paper we proposed a framework for workflow scheduling in the cloud environment, in which the core component is the middleware called broker mediating the interaction between users and cloud service providers. To process jobs in on-demand and virtualized resources from cloud service providers, many papers propose scheduling algorithms that allocate jobs to virtual machines which are dedicated to one machine one job. With this method, the isolation of being processed jobs is guaranteed, but we can’t use each resource to its fullest computing capacity with high efficiency in resource utilization. This paper therefore proposed a cost-efficient job scheduling algorithm which maximizes the utilization of managed resources with increasing the degree of multiprogramming to reduce the number of needed virtual machines; consequently we can save the cost for processing requests. We also consider the performance degradation in proposed scheme with thrashing and context switching. By evaluating the experimental results, we have shown that the proposed scheme has better cost-performance feature compared to an existing scheme.

클라우드 컴퓨팅 환경에서 강화학습기반 자원할당 기법

최영호(Yeongho Choi),임유진(Yujin Lim),박재성(Jaesung Park) 한국통신학회 2015 韓國通信學會論文誌 Vol.40 No.4